Marketing the Unfamiliar: The Role of Context and Item-Specific Information in Electronic Agent Recommendations

By Alan D. J. Cooke, H. Sujan, Mita Sujan, Barton A. Weitz. 2002

122 citations recorded by the source corpus.

Subfields: Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/94f94def392622377a75c19290713d95d40e92f1

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Citations in the corpus

Inspectability and control in social recommenders (2012, background): “Arguably, this recognition effect is an important aspect of inspectability, because knowing recommendations may raise users’ trust in the recommender [7, 42].”
The nice thing about context is that everyone has it (2015, methodology): “Across subfields like ubiquitous computing and recommender systems research, methods for defining, apprehending, and using ‘context’ are active areas of study (Adomavicius and Tuzhilin, 2011; Anand and Mobasher, 2007; Cooke et al., 2002; Dey, 2001; Lathia, 2014; Panniello et al., 2014).”
Explaining the user experience of recommender systems (2012, background): “Whereas a substantial amount of research considers the individual qualities of these recommendations, little research has considered the composition of the list (Hu and Pu 2011; Chen and Pu 2012; Ziegler et al. 2005; Cooke et al. 2002).”
Moving from Free to Fee: How Online Firms Market to Change Their business Model Successfully. (2008, background): “Recent research in marketing has made significant advances in the understanding of various Internet marketing issues, including Web-browsing behavior (Bucklin and Sismeiro 2003), search-engine visits (Telang, Boatwright, and Mukhopadhyay 2004), and recommendation agents (Cooke et al. 2002; Diehl, Ko”
Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity (2009, background): “Cooke et al. (2002) examine how purchase decisions under recommendations depend on the information provided, context, and familiarity.”
Beyond nudges: Tools of a choice architecture (2012, background): “However, these tools can also predictably influence consumers’ choices through very subtle architectural features such as the set of other products that are presented alongside a recommended alternative (Cooke et al. 2002) or which product attributes are made more salient by the system (Häubl and Mu”
Recommender Systems (2012, background): “It has been shown that to facilitate users’ acceptance of recommendations, the recommendations need to be transparent [45,46]: users appreciate when it is clear why a particular item has been recommended to them.”
Creating Effective Online Customer Experiences (2018, background): “Providing more attribute information generally increases product evaluations and purchase likelihood (Cooke et al. 2002; Hauser et al. 2009).”
Developing a Price-Sensitive Recommender System to Improve Accuracy and Business Performance of Ecommerce Applications (2015, background): “Some scholars studied what factors affect an RS adoption by customers 9, 10 .”